{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Titanic competition is a very good way to introduce feature engineering and classification models. I'm gonna explore the data and make something with them and also imput missing values. Feature engineering is an important part of machine learning process so I want to spend more time for this part. I'm gonna try I few models and tell you which work the best with train dataset from this competition. Please consider upvoting if this is useful to you :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math \n",
    "import xgboost as xgb\n",
    "np.random.seed(2019)\n",
    "from scipy.stats import skew\n",
    "from scipy import stats\n",
    "\n",
    "import statsmodels\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm adding here 'train' variable in order to check in the easiest way later which observations are from train and test dataset because I'm gonna join train and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_and_concat_dataset(training_path, test_path):\n",
    "    train = pd.read_csv(training_path)\n",
    "    train['train'] = 1\n",
    "    test = pd.read_csv(test_path)\n",
    "    test['train'] = 0\n",
    "    data = train.append(test, ignore_index=True)\n",
    "    return train, test, data\n",
    "\n",
    "train, test, data = read_and_concat_dataset('train.csv', 'test.csv')\n",
    "data = data.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##**Explore the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>train</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0   NaN        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0   NaN        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket  train  \n",
       "PassengerId                                                    \n",
       "1              male      1       0.0         A/5 21171      1  \n",
       "2            female      1       1.0          PC 17599      1  \n",
       "3            female      0       1.0  STON/O2. 3101282      1  \n",
       "4            female      1       1.0            113803      1  \n",
       "5              male      0       0.0            373450      1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.680672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.466394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch       Pclass        SibSp  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027     2.294882     0.498854   \n",
       "std      14.413493    51.758668     0.865560     0.837836     1.041658   \n",
       "min       0.170000     0.000000     0.000000     1.000000     0.000000   \n",
       "25%      21.000000     7.895800     0.000000     2.000000     0.000000   \n",
       "50%      28.000000    14.454200     0.000000     3.000000     0.000000   \n",
       "75%      39.000000    31.275000     0.000000     3.000000     1.000000   \n",
       "max      80.000000   512.329200     9.000000     3.000000     8.000000   \n",
       "\n",
       "         Survived        train  \n",
       "count  891.000000  1309.000000  \n",
       "mean     0.383838     0.680672  \n",
       "std      0.486592     0.466394  \n",
       "min      0.000000     0.000000  \n",
       "25%      0.000000     0.000000  \n",
       "50%      0.000000     1.000000  \n",
       "75%      1.000000     1.000000  \n",
       "max      1.000000     1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PassengerId** - the unique id of the row, it doesn't have any effect on Survived value.\n",
    "\n",
    "**Survived** - binary:\n",
    "* 1 -> Survived\n",
    "* 0 -> Not survived\n",
    "\n",
    "**Pclass** (Passenger Class) - economic status of the passenger, this variable has 3 values;\n",
    "* 1 -> Upper Class\n",
    "* 2 -> Middle Class\n",
    "* 3 -> Lower Class\n",
    "\n",
    "**Name**, **Sex** and **Age** - are self-explanatory.\n",
    "\n",
    "**SibSp** - the total number of the passengers' siblings and spouse.\n",
    "\n",
    "**Parch** - the total number of the passengers' parents and children.\n",
    "\n",
    "**Ticket** - the ticket number.\n",
    "\n",
    "**Fare** - the passenger fare.\n",
    "\n",
    "**Cabin** - the cabin number.\n",
    "\n",
    "**Embarked** is port of embarkation, 3 values:\n",
    "* C -> Cherbourg\n",
    "* Q -> Queenstown\n",
    "* S -> Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation matrix between numerical values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " g = sns.heatmap(data[[\"Survived\",\"SibSp\",\"Parch\",\"Age\",\"Fare\"]].corr(),annot=True, cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlations between numerical variables and Survived aren't so high but it doesn't mean that the other features are not useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparing(data,variable1, variable2):\n",
    "    print(data[[variable1, variable2]][data[variable2].isnull()==False].groupby([variable1], as_index=False).mean().sort_values(by=variable2, ascending=False))\n",
    "    g = sns.FacetGrid(data, col=variable2).map(sns.distplot, variable1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def counting_values(data, variable1, variable2):\n",
    "    return data[[variable1, variable2]][data[variable2].isnull()==False].groupby([variable1], as_index=False).mean().sort_values(by=variable2, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parch vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Parch  Survived\n",
      "3      3  0.600000\n",
      "1      1  0.550847\n",
      "2      2  0.500000\n",
      "0      0  0.343658\n",
      "5      5  0.200000\n",
      "4      4  0.000000\n",
      "6      6  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Parch','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SibSp vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SibSp  Survived\n",
      "1      1  0.535885\n",
      "2      2  0.464286\n",
      "0      0  0.345395\n",
      "3      3  0.250000\n",
      "4      4  0.166667\n",
      "5      5  0.000000\n",
      "6      8  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'SibSp','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Fare  Survived\n",
      "247  512.3292       1.0\n",
      "196   57.9792       1.0\n",
      "89    13.8583       1.0\n",
      "88    13.7917       1.0\n",
      "86    13.4167       1.0\n",
      "..        ...       ...\n",
      "103   15.5500       0.0\n",
      "180   47.1000       0.0\n",
      "179   46.9000       0.0\n",
      "178   42.4000       0.0\n",
      "124   21.0750       0.0\n",
      "\n",
      "[248 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Fare','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Age  Survived\n",
      "0    0.42       1.0\n",
      "9    5.00       1.0\n",
      "79  63.00       1.0\n",
      "68  53.00       1.0\n",
      "1    0.67       1.0\n",
      "..    ...       ...\n",
      "37  28.50       0.0\n",
      "49  36.50       0.0\n",
      "46  34.50       0.0\n",
      "26  20.50       0.0\n",
      "30  23.50       0.0\n",
      "\n",
      "[88 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Age','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sex vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counting_values(data, 'Sex','Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Women  Survived\n",
      "1      1  0.742038\n",
      "0      0  0.188908\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Women'] = np.where(data.Sex=='female',1,0)\n",
    "comparing(data, 'Women','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pclass vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Survived\n",
      "0       1  0.629630\n",
      "1       2  0.472826\n",
      "2       3  0.242363\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Pclass','Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba2067e978>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data, col='Survived', row='Pclass', size=2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embarked vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba20a04438>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data, row='Embarked', col='Survived', size=2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##**Missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age          263\n",
       "Cabin       1014\n",
       "Embarked       2\n",
       "Fare           1\n",
       "Name           0\n",
       "Parch          0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "SibSp          0\n",
       "Survived     418\n",
       "Ticket         0\n",
       "train          0\n",
       "Women          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 263 missing ages, 1014 missing cabins. Age is very important variable, so it's worth spending time to imput them. If it comes to imputing cabins - it's too hard to do because dataset has only 1309 observations so 77% cabins are missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values in Embarked and Fare variables are very easy to imput because we can use the most popular value or something like that.\n",
    "\n",
    "I'm gonna replace missing value in Fare with 0 and in Embarked with the most popular value ('S')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    87.508992\n",
       "2    21.179196\n",
       "3    13.302889\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Pclass').Fare.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Fare = data.Fare.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S    914\n",
      "C    270\n",
      "Q    123\n",
      "Name: Embarked, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.Embarked.value_counts())\n",
    "data.Embarked = data.Embarked.fillna('S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it comes to Cabin variable, I'm gonna fill up NaN values with 'Unknown' and get first letter from every Cabin in dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Cabin = data.Cabin.fillna('Unknown')\n",
    "data['Cabin'] = data['Cabin'].str[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the distribution of the cabins in individual passenger classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Cabin\n",
       "1       C         94\n",
       "        U         67\n",
       "        B         65\n",
       "        D         40\n",
       "        E         34\n",
       "        A         22\n",
       "        T          1\n",
       "2       U        254\n",
       "        F         13\n",
       "        D          6\n",
       "        E          4\n",
       "3       U        693\n",
       "        F          8\n",
       "        G          5\n",
       "        E          3\n",
       "Name: Cabin, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Pclass').Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cabin 'Unknown' will be set to C for the first class, D for the second class and G for the third class. One observation with Cabin 'T' and first class I'll fix with C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Cabin'] = np.where((data.Pclass==1) & (data.Cabin=='U'),'C',\n",
    "                                            np.where((data.Pclass==2) & (data.Cabin=='U'),'D',\n",
    "                                                                        np.where((data.Pclass==3) & (data.Cabin=='U'),'G',\n",
    "                                                                                                    np.where(data.Cabin=='T','C',data.Cabin))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm gonna get title from each Name in dataset. This variable will be very useful and it can help to imput missing value in Age. People's titles can represent their age, earnings and life status and all these three properties can be associated with the possibility of survival on a ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Title'] = data.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(data['Title'], data['Sex'])\n",
    "data = data.drop('Name',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need to replace a few titles with 'other' values because these titles are not as popular and have a low frequency of occurrence in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's replace a few titles -> \"other\" and fix a few titles\n",
    "data['Title'] = np.where((data.Title=='Capt') | (data.Title=='Countess') | (data.Title=='Don') | (data.Title=='Dona')\n",
    "                        | (data.Title=='Jonkheer') | (data.Title=='Lady') | (data.Title=='Sir') | (data.Title=='Major') | (data.Title=='Rev') | (data.Title=='Col'),'Other',data.Title)\n",
    "\n",
    "data['Title'] = data['Title'].replace('Ms','Miss')\n",
    "data['Title'] = data['Title'].replace('Mlle','Miss')\n",
    "data['Title'] = data['Title'].replace('Mme','Mrs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how the distribution of survival variable  depending on the title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 394.125x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n",
    "facet = sns.FacetGrid(data = data, hue = \"Title\", legend_out=True, size = 4.5)\n",
    "facet = facet.map(sns.kdeplot, \"Age\")\n",
    "facet.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People with 'Master' have the highest survival rate. Maybe because people with the master are mainly boys under 13 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see distributions on box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba20311198>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = data, x = \"Title\", y = \"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba20481eb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 699.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(data, hue=\"Survived\",aspect=3)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, data['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age has a very large impact on the survival rate, but when this variable has missing values - it is useless. I'm gonna impute the missing values using the average age values in particular groups due to the titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Dr        43.571429\n",
       "Master     5.482642\n",
       "Miss      21.824366\n",
       "Mr        32.252151\n",
       "Mrs       36.918129\n",
       "Other     45.714286\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Title').Age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = np.where((data.Age.isnull()) & (data.Title=='Master'),5,\n",
    "                        np.where((data.Age.isnull()) & (data.Title=='Miss'),22,\n",
    "                                 np.where((data.Age.isnull()) & (data.Title=='Mr'),32,\n",
    "                                          np.where((data.Age.isnull()) & (data.Title=='Mrs'),37,\n",
    "                                                  np.where((data.Age.isnull()) & (data.Title=='Other'),45,\n",
    "                                                           np.where((data.Age.isnull()) & (data.Title=='Dr'),44,data.Age))))))                   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few new variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##**Feature engineering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* FamilySize - number of family members, people travelling alone will have a value of 1\n",
    "* Women - it depends on Sex variable but I'm making it in binary way\n",
    "* Mother - women with Mrs title and at least 1 parch, women, children and mothers probably have a survival factor\n",
    "* Free - people who don't need to pay fare, these people could win tickets or something like that, they can have a similar survival rate\n",
    "* TypeOfTicket - prefixes of ticket, tickets with same prefixes may have a similar class and survival.\n",
    "\n",
    "If it comes to TypeOfTicket variable I'm gonna also replace a few values of this variable with 'other' values, relying on the same as in the case of titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['FamilySize'] = data.SibSp + data.Parch + 1\n",
    "data['Mother'] = np.where((data.Title=='Mrs') & (data.Parch >0),1,0)\n",
    "data['Free'] = np.where(data['Fare']==0, 1,0)\n",
    "data = data.drop(['SibSp','Parch','Sex'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "TypeOfTicket = []\n",
    "for i in range(len(data.Ticket)):\n",
    "    ticket = data.Ticket.iloc[i]\n",
    "    for c in string.punctuation:\n",
    "                ticket = ticket.replace(c,\"\")\n",
    "                splited_ticket = ticket.split(\" \")   \n",
    "    if len(splited_ticket) == 1:\n",
    "                TypeOfTicket.append('NO')\n",
    "    else: \n",
    "                TypeOfTicket.append(splited_ticket[0])\n",
    "            \n",
    "data['TypeOfTicket'] = TypeOfTicket\n",
    "\n",
    "data.TypeOfTicket.value_counts()\n",
    "data['TypeOfTicket'] = np.where((data.TypeOfTicket!='NO') & (data.TypeOfTicket!='PC') & (data.TypeOfTicket!='CA') & \n",
    "                                (data.TypeOfTicket!='A5') & (data.TypeOfTicket!='SOTONOQ'),'other',data.TypeOfTicket)\n",
    "data = data.drop('Ticket',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FamilySize vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   FamilySize  Survived\n",
      "3           4  0.724138\n",
      "2           3  0.578431\n",
      "1           2  0.552795\n",
      "6           7  0.333333\n",
      "0           1  0.303538\n",
      "4           5  0.200000\n",
      "5           6  0.136364\n",
      "7           8  0.000000\n",
      "8          11  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'FamilySize','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Title vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "4     Mrs  0.793651\n",
       "2    Miss  0.702703\n",
       "1  Master  0.575000\n",
       "0      Dr  0.428571\n",
       "5   Other  0.312500\n",
       "3      Mr  0.156673"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counting_values(data, 'Title','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TypeOfTicket vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TypeOfTicket</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PC</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NO</td>\n",
       "      <td>0.383459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>0.341463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>other</td>\n",
       "      <td>0.337079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A5</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TypeOfTicket  Survived\n",
       "3           PC  0.650000\n",
       "2           NO  0.383459\n",
       "1           CA  0.341463\n",
       "5        other  0.337079\n",
       "4      SOTONOQ  0.133333\n",
       "0           A5  0.095238"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counting_values(data, 'TypeOfTicket','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabin vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.492537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin  Survived\n",
       "4     E  0.750000\n",
       "1     B  0.744681\n",
       "5     F  0.615385\n",
       "2     C  0.540000\n",
       "3     D  0.492537\n",
       "0     A  0.466667\n",
       "6     G  0.238095"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counting_values(data, 'Cabin','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mother vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Mother  Survived\n",
      "1       1  0.714286\n",
      "0       0  0.361677\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Mother','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Free vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Free  Survived\n",
      "0     0  0.389269\n",
      "1     1  0.066667\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparing(data, 'Free','Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm cutting Age variable to 5 equal intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0,12,24,45,60,data.Age.max()]\n",
    "labels = ['Child', 'Young Adult', 'Adult','Older Adult','Senior']\n",
    "data[\"Age\"] = pd.cut(data[\"Age\"], bins, labels = labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I create dummy variables for all variables with categories using the function get_dummies from pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##**Modeling**\n",
    "* Decision Tree Classifier\n",
    "* Random Forest Classifier\n",
    "* KNeighbors Classifier\n",
    "* SVM\n",
    "* Logistic Regression\n",
    "* XGB Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check how good each model is I'm gonna split dataset to train (70%) and test (30%) dataset (excluding missing values in Survived variable) and use Accuracy Score from sklearn.metrics. I set random_state to 2019 in order to compare the results between the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "trainX, testX, trainY, testY = train_test_split(data[data.Survived.isnull()==False].drop('Survived',axis=1),data.Survived[data.Survived.isnull()==False],test_size=0.30, random_state=2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm gonna to put result of each model in Data Frame 'Results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Results = pd.DataFrame({'Model': [],'Accuracy Score': [], 'Recall':[], 'F1score':[]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(max_depth=4)\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "res = pd.DataFrame({\"Model\":['DecisionTreeClassifier'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>163</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        163   13\n",
       "1.0         32   60"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=2500, max_depth=4)\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "from sklearn.metrics import accuracy_score\n",
    "res = pd.DataFrame({\"Model\":['RandomForestClassifier'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>160</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>27</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        160   16\n",
       "1.0         27   65"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**KNeighbors Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "from sklearn.metrics import accuracy_score\n",
    "res = pd.DataFrame({\"Model\":['KNeighborsClassifier'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>138</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>34</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        138   38\n",
       "1.0         34   58"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "from sklearn.metrics import accuracy_score\n",
    "res = pd.DataFrame({\"Model\":['SVC'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>131</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        131   45\n",
       "1.0         16   76"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "from sklearn.metrics import accuracy_score\n",
    "res = pd.DataFrame({\"Model\":['LogisticRegression'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>153</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        153   23\n",
       "1.0         22   70"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XGB Classifier**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I put here some hyper-parameters tuning with n_estmators, max_depth and learning_rate parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "model = XGBClassifier(learning_rate=0.001,n_estimators=2500,\n",
    "                                max_depth=4, min_child_weight=0,\n",
    "                                gamma=0, subsample=0.7,\n",
    "                                colsample_bytree=0.7,\n",
    "                                scale_pos_weight=1, seed=27,\n",
    "                                reg_alpha=0.00006)\n",
    "model.fit(trainX, trainY)\n",
    "y_pred = model.predict(testX)\n",
    "from sklearn.metrics import accuracy_score\n",
    "res = pd.DataFrame({\"Model\":['XGBClassifier'],\n",
    "                    \"Accuracy Score\": [accuracy_score(y_pred,testY)],\n",
    "                   \"Recall\": [recall_score(testY, y_pred)],\n",
    "                   \"F1score\": [f1_score(testY, y_pred)]})\n",
    "Results = Results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>159</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Real data          \n",
       "0.0        159   17\n",
       "1.0         23   69"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(testY, y_pred, rownames=['Real data'], colnames=['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##**Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.832090</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.839552</td>\n",
       "      <td>0.706522</td>\n",
       "      <td>0.751445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.731343</td>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.617021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.772388</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.713615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.832090</td>\n",
       "      <td>0.760870</td>\n",
       "      <td>0.756757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.850746</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.775281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy Score    Recall   F1score\n",
       "0  DecisionTreeClassifier        0.832090  0.652174  0.727273\n",
       "0  RandomForestClassifier        0.839552  0.706522  0.751445\n",
       "0    KNeighborsClassifier        0.731343  0.630435  0.617021\n",
       "0                     SVC        0.772388  0.826087  0.713615\n",
       "0      LogisticRegression        0.832090  0.760870  0.756757\n",
       "0           XGBClassifier        0.850746  0.750000  0.775281"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How we see - XGB Classifier gives the best results. This model helps me to get 0.81339 on competition test dataset and it gives me place in 5% best results on Leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done1\n"
     ]
    }
   ],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "trainX = data[data.Survived.isnull()==False].drop(['Survived','train'],axis=1)\n",
    "trainY = data.Survived[data.Survived.isnull()==False]\n",
    "testX = data[data.Survived.isnull()==True].drop(['Survived','train'],axis=1)\n",
    "model = XGBClassifier(learning_rate=0.001,n_estimators=2500,\n",
    "                                max_depth=4, min_child_weight=0,\n",
    "                                gamma=0, subsample=0.7,\n",
    "                                colsample_bytree=0.7,\n",
    "                                scale_pos_weight=1, seed=27,\n",
    "                                reg_alpha=0.00006)\n",
    "model.fit(trainX, trainY)\n",
    "test = data[data.train==0]\n",
    "test['Survived'] = model.predict(testX).astype(int)\n",
    "test = test.reset_index()\n",
    "test[['PassengerId','Survived']].to_csv(\"submissionXGB.csv\",index=False)\n",
    "print(\"done1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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